1 Live Demo

We will demonstrate how to produce numerical summaries for iris.

# quantitative variable
mean(iris$Sepal.Length)
median(iris$Sepal.Length)
summary(iris$Sepal.Length)  

# qualitative variable
table(iris$Species) 

# qualitative variable + quantitative variable
table(iris$Species, iris$Sepal.Length)

We will demonstrate how to import data into RStudio, using the Australian road fatalities data.

# import from a url.
road = read.csv("http://www.maths.usyd.edu.au/u/UG/JM/DATA1001/r/current/data/2016Fatalities.csv")

# import data from a folder
# getwd()  # this checks the working directory
road1 = read.csv("/data/2016Fatalities.csv")

2 Summary

You are beginning to learn how to:

pose research questions
choose what graphical and numerical summaries are appropriate for given variables
interpret the output.

Type of Variable	Referred to in R
Qualitative / Categorical	`factor`
Quantitative / Numerical	`num`

Type of Data	Type of Graphical Summary	In base R
1 Qualitative Variable	Barplot	`barplot()`
2 Qualitative Variables	Double (clustered) Barplot	`barplot()`
1 Quantitative Variable	Histogram or Boxplot	`hist()`, `boxplot()`
2 Quantitative Variables	Scatterplot	`plot()`
1 Quantitative & 1 Qualitative Variable	Double (comparative) boxplot	`boxplot()`

3 Have a go in base R

Now you’re ready to try some interesting data! Don’t get bamboozled by all the code, rather see what everything does!
Consider the Australian road fatalities from 1989 (a bigger version of the data used in Week 2 lectures). The data is sourced from BITRE.

3.1 Initial Data Analysis

Upload the data.

# Read data from url into R
road = read.csv("http://www.maths.usyd.edu.au/u/UG/JM/DATA1001/r/current/data/AllFatalities.csv")

Note: An alternative way is to download the data from Canvas, store the data in DATA1001files/data and upload from there. You will need to use this method in future projects, when you upload your own data.

# Read data from url into R
road = read.csv("data/AllFatalities.csv",header=T)

Produce a snapshot of the data.

str(road)

3.2 Research Questions

3.2.1 Were there more fatalities on a certain day of the week?

Here we consider 1 qualitative variable: the road fatalities across the days of the week.
1st isolate the variable Dayweek. Check how R classifies it. Produce a barplot. What is annoying about it?

class(road$Dayweek)

## [1] "factor"

barplot(table(road$Dayweek))

We can re-order the categories for dayweek and the produce a barplot. What pattern emerges? Suggest possible reasons for it?

orderdayweek = ordered(road$Dayweek, levels=c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
barplot(table(orderdayweek))

barplot(table(orderdayweek),las=2)

3.2.2 Was there any pattern in how buses were involved in fatalities, on different days of the week?

Here we consider 2 qualitative variables: the road fatalities across the days of the week, cross-classified by bus involvement.
Is there any pattern?

road1 = table(road$Bus_Involvement, road$Dayweek)
road1

##      
##       Friday Monday Saturday Sunday Thursday Tuesday Wednesday
##   No    7520   5216     8496   7382     6155    5258      5727
##   Yes    178    125      108     80      129     123       127

barplot(road1, main = "Fatalities by Day of the Week and Bus Involvement", xlab = "Day of the week", 
    col = c("lightblue", "lightgreen"), legend = rownames(road1))

3.2.3 Was there any pattern in how heavy rigid trucks were involved in fatalities, on different days of the week?

Here we consider 2 qualitative variables: the road fatalities across the days of the week, cross-classified by heavy rigid truck involvement.
Investigate whether the involvement of heavy rigid trucks differs across the days?

road2 = table(road$Hvy_Rigid_Truck_Involvement, road$Dayweek)
road2

##      
##       Friday Monday Saturday Sunday Thursday Tuesday Wednesday
##   -9    4454   3080     4968   4299     3569    3042      3353
##   No    3019   2096     3505   3091     2496    2164      2283
##   Yes    225    165      131     72      219     175       218

barplot(road2, main = "Fatalities by Day of the Week and Heavy Rigid Involvement", xlab = "Day of the week", 
    col = c("lightpink","lightblue", "lightgreen"), legend = rownames(road2))

3.2.4 Were there more fatalities in certain age groups?

Here we consider 1 quantitative variable: fatalities.
1st isolate the variable Age. How does R classify it?

class(road$Age)

## [1] "factor"

Change the classification to a quantitative variable.

road$Age = as.numeric(as.character(road$Age))

## Warning: NAs introduced by coercion

class(road$Age)

## [1] "numeric"

Produce graphical summaries. What patterns are revealed?

hist(road$Age, prob=T)

boxplot(road$Age)

We can customise the plots.

hist(road$Age,freq=FALSE,main="Histogram",ylab="Probabilities", col="green")

boxplot(road$Age,horizontal=TRUE,col="red")

3.2.5 Does biological sex affect the number of fatalities across age groups?

Here we consider 1 quantitative variable divided by 1 qualitative variable.
Control for biological sex - ie consider fatalities by age divided by biological sex.

ageF = road$Age[road$Gender == "Female"]
ageM = road$Age[road$Gender == "Male"]
par(mfrow = c(2, 1))
boxplot(ageF,horizontal=T, col="light blue")
boxplot(ageM,horizontal=T)

You can put 2 plots next to each other.

par(mfrow=c(1,2))
boxplot(ageF,horizontal=T, col="light blue")
boxplot(ageM,horizontal=T)

3.2.6 Explore

Explore another variable.

4 Now try in ggplot

1st, read through this Overview and re-read RGuide Chapter 5.

2nd, load the package ggplot2 or tidyverse (which includes ggplot2).

road1 = read.csv("http://www.maths.usyd.edu.au/u/UG/JM/DATA1001/r/current/data/AllFatalities.csv")  # Start again with the raw data frame
library(tidyverse)

## Warning: package 'tidyverse' was built under R version 3.6.3

## -- Attaching packages ------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.0     v purrr   0.3.3
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## Warning: package 'ggplot2' was built under R version 3.6.3

## -- Conflicts ---------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

str(road1)

## 'data.frame':    46624 obs. of  18 variables:
##  $ CrashID                      : num  4.2e+12 2.2e+12 1.2e+12 5.2e+12 6.2e+12 ...
##  $ State                        : Factor w/ 8 levels "ACT","NSW","NT",..: 5 7 2 8 6 6 6 4 7 8 ...
##  $ Date                         : Factor w/ 9739 levels "01-Apr-00","01-Apr-01",..: 125 125 444 444 444 444 760 760 1080 1080 ...
##  $ Day                          : int  1 1 2 2 2 2 3 3 4 4 ...
##  $ Month                        : Factor w/ 12 levels "April","August",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ Year                         : int  2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 ...
##  $ Dayweek                      : Factor w/ 7 levels "Friday","Monday",..: 1 1 3 3 3 3 4 4 2 2 ...
##  $ Time                         : Factor w/ 1385 levels "0:00","0:01",..: 59 795 30 553 710 710 231 354 712 461 ...
##  $ Hour                         : int  1 20 0 17 19 19 11 14 2 15 ...
##  $ Minute                       : int  0 30 30 20 58 58 55 0 0 47 ...
##  $ Crash_Type                   : Factor w/ 3 levels "Multiple","Pedestrian",..: 3 3 3 1 1 1 1 2 3 3 ...
##  $ Bus_Involvement              : Factor w/ 2 levels "No ","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Hvy_Rigid_Truck_Involvement  : Factor w/ 3 levels "-9","No ","Yes": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Articulated_Truck_Involvement: Factor w/ 2 levels "No ","Yes": 1 1 1 2 1 1 1 1 1 1 ...
##  $ Speed_Limit                  : Factor w/ 19 levels "10","100","110",..: 3 15 2 3 15 15 2 11 10 3 ...
##  $ Road_User                    : Factor w/ 8 levels "-9","Bicyclist",..: 3 5 7 3 5 4 5 8 3 5 ...
##  $ Gender                       : Factor w/ 3 levels "Female","Male",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Age                          : Factor w/ 102 levels "0","1","10","100",..: 37 20 12 51 11 27 49 70 62 66 ...

Redo the Road Fatalities exercises using ggplot.

4.1 Research Questions

4.1.1 Were there more fatalities on a certain day of the week?

Here we consider 1 qualitative variable: the road fatalities across the days of the week.

p = ggplot(road1, aes(x = Dayweek))  # Defines the x axis (1 variable).
p + geom_bar()

road1$Dayweek = factor(road1$Dayweek, levels=c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
p = ggplot(road1, aes(x = Dayweek))  
p + geom_bar()

4.1.2 Was there any pattern in how buses were involved in fatalities, on different days of the week?

Here we consider 2 qualitative variables: the road fatalities across the days of the week, cross-classified by bus involvement.

p + geom_bar(aes(fill=Bus_Involvement))

4.1.3 Was there any pattern in how heavy rigid trucks were involved in fatalities, on different days of the week?

Here we consider 2 qualitative variables: the road fatalities across the days of the week, cross-classified by heavy rigid truck involvement.
Investigate whether the involvement of heavy rigid trucks differs across the days?

p + geom_bar(aes(fill=Hvy_Rigid_Truck_Involvement))

4.1.4 Were there more fatalities in certain age groups?

Here we consider 1 quantitative variable: fatalities.

# Change classification of Age variable (factor -> integer)
class(road1$Age)

## [1] "factor"

road1$Age = as.numeric(as.character(road1$Age))

## Warning: NAs introduced by coercion

class(road1$Age)

## [1] "numeric"

# Histogram
p1 = ggplot(road1, aes(x = Age))  
p1 + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 82 rows containing non-finite values (stat_bin).

# Boxplot
# Note for a simple boxplot, you need to make the x-axis empty.
p2 = ggplot(road1, aes(x="",y=Age))
p2 + geom_boxplot()

## Warning: Removed 82 rows containing non-finite values (stat_boxplot).

4.1.5 Does biological sex affect the number of fatalities across age groups?

Here we consider 1 quantative variable divided by 1 qualitative variable.
Control for biological sex - ie consider fatalities by age divided by biological sex.

p3 = ggplot(road1, aes(x = Gender,y = Age))  
p3 + geom_boxplot()

## Warning: Removed 82 rows containing non-finite values (stat_boxplot).

5 Coding Challenge

Using ggplot create a barplot of crashes with month on the x axis (hint: You’ll have to reorder the month variable similar to how we reordered the DayWeek Variable) and facet by gender.

Which gender had the higher number of crashes? Is this what you expected?

road1$Month = factor(road1$Month, levels=c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", 'November', 'December'))
ggplot(road1, aes(x = Month))+
geom_bar(aes(fill = Gender))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

6 DATA1901 Extension (plotly)

Here we introduce the cool interactive tool called plotly, which is automatically part of the ggplot2 package.

Work through the RGuide 5.7.
Now try some plots with the Road Fatality data.

library('plotly')

## Warning: package 'plotly' was built under R version 3.6.3

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

p4 = plot_ly(road1, x = ~Age, color = ~Gender, type = 'box') 
p4

## Warning: Ignoring 82 observations

DATA1001/1901 Lab3

Graphical and Numerical Summaries

© University of Sydney 2021